Method for configuring a RF transmit assembly

ABSTRACT

The present disclosure relates to a method for configuring a radio frequency, RF, transmit assembly (200) for a magnetic resonance imaging system (300) for acquiring magnetic resonance imaging data from a subject within an imaging zone using an RF pulse sequence, the RF transmit assembly (200) comprising an RF amplifier (215) and a transmit coil (213), wherein the RF transmit assembly (200) is configurable with a set of configuration parameters. The method comprises: providing (3001) operating conditions of the RF transmit assembly, the operating conditions being indicative of at least: a property of the RF pulse sequence and a measurable parameter that influences the RF pulse sequence property when operating the RF transmit assembly using the RF pulse sequence; using (3003) a predefined machine learning model for determining, for the operating conditions, at least part of the set of configuration parameters and associated values; configuring (3005) the RF transmit assembly (200) in accordance with the determined configuration parameters and associated values.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase application of InternationalApplication No. PCT/EP2019/064742 filed on Jun. 6, 2019, which claimsthe benefit of EP Application Serial No. 18176930.8 filed Jun. 11, 2018and is incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to scanning imaging systems, in particular to amethod for configuring a radio frequency (RF) transmit assembly.

BACKGROUND OF THE INVENTION

Transmit RF chains are configured to generate a desired spin excitationin a minimum period of time, while complying with technological andbiological limits. The transmit RF chain of a magnetic resonance imaging(MRI) system contains one or more RF amplifiers which drive a MRI coil.The RF chain may consist of a digital RF pulse generator, a RFamplifier, analogue T/R switches and the MRI antenna. However, there isa continuous need to improve the control of the operation of the RFchains.

SUMMARY OF THE INVENTION

Various embodiments provide for a method for configuring a RF transmitassembly, medical analysis system, and computer program product, asdescribed by the subject matter of the independent claims. Advantageousembodiments are described in the dependent claims.

The present disclosure relates to a hardware and software required torealize a MRI system with optimized RF transmit chain or assembly. A MRIRF transmit assembly is provided with a machine learning module thatfeatures machine learning. The module may use different machine learningalgorithms for different RF assembly optimization such as pulsegeneration, digital pre-distortion, parameter setting of RF amplifier,impedance matching option and RF switch toggle and selection of the shimset in multi-channel TX systems. The different machine learningalgorithms may have as input the current operating conditions of the RFtransmit assembly. For the complete RF assembly, a set of learningalgorithms for machine learning may be applied. Learning methods andstrategies may be based on a cloud based implementation or localimplementation. The machine-learning module is suited for differentscenarios depending on the available data and amount of training time.

In one aspect, the invention relates to a method for configuring a radiofrequency, RF, transmit assembly for a magnetic resonance imaging systemfor acquiring magnetic resonance imaging data from a subject within animaging zone using a RF pulse sequence. The RF transmit assemblycomprises a RF amplifier and a transmit coil, wherein the RF transmitassembly is configurable with a set of configuration parameters. Themethod comprises: providing operating conditions of the RF transmitassembly. The operating conditions are indicative of at least: aproperty of the RF pulse sequence and a measurable parameter thatinfluences the RF pulse sequence property when operating the RF transmitassembly using the RF pulse sequence. A predefined machine learningmodel may be used for determining at least part of the set ofconfiguration parameters and associated values for the operatingconditions. The RF transmit assembly may be configured in accordancewith the determined configuration parameters.

The provided operating conditions may be currently measured operatingconditions while the RF transmit assembly is operating using the RFpulse sequence. In another example, the provided operating conditionsmay be desired operating conditions for which the RF transmit assemblyis to be configured in order to function accordingly.

The term “machine learning” refers to a computer algorithm used toextract useful information from training data by building probabilisticmodels (referred to as machine learning (or learned) models) in anautomated way. The machine learning may be performed using one or morelearning algorithms such as linear regression, K-means, classificationalgorithm, reinforcement algorithm etc. A “model” may for example be anequation or set of rules that makes it possible to predict an unmeasuredvalue (e.g. which configuration corresponds to a given operatingcondition) from other, known values and/or to predict or select anaction to maximize a future reward or minimize a future penalty.According to one embodiment, the machine learning model is a deeplearning model.

An ad-hoc analytic optimization, e.g. by a user, of dozens of input andoutput parameters within a given timeframe is impossible considering theamount of parameters and their non-linear (e.g. amplifier gain, fixparameter limits) interdependencies. The present method may enable tofind the optimal configuration set for the RF transmit assembly based ona multi-dimensional approach involving multiple input parameters,wherein said parameters may be interdependent.

The present disclosure may enable to automate the operation of the RFtransmit assembly. This may reduce the need for operator interventionfor the configuration of the RF transmit assembly.

The present disclosure may further have the advantage to use thesuitable configuration that is accurately defined in advance and maythus provide a cleaner RF transmit signal with less distortion and amore stable transmit signal in respect of phase and amplitude, relievethe RF-amplifier from internal stabilization circuitry, and reducecosts. According to one embodiment, the property of the pulse sequencecomprises at least one of a: peak power, average power, pulse shape,total duration and frequency distribution, linearity. The property ofthe pulse sequence may enable to control the RF pulse sequence for theacquisition of the MR data. Such a control based on many properties maybe virtually impossible to optimize manually.

According to one embodiment, the measurable parameter comprises at leastone of ambient temperature, temperature of a cooling water of the RFtransmit assembly, junction temperatures of components of the RFamplifier (e.g. temperature of RF LDMOS or temperature of heatsink),coil load of the transmit coil, power supply voltage, a power supply ofthe RF transmit assembly. The operating conditions may further beindicative of a subject (e.g. patient) weight, a subject height, imagingposition, an anatomy to be scanned. The measurable parameter may furtherindicate B1 maps, B1 demands, status of capacitor banks or pick up loopsignals. The transmit coil of the RF transmit assembly may be configuredto transmit electromagnetic waves into a sample, thereby creatingoscillating B1 magnetic field needed to excite nuclear spins. The B1maps and B1 demands may be quantified be measurable parameter. Ameasurable parameter may for example be derived or determined from otherone or more measurable parameters.

The more properties and measurable parameters used in the determinationof the operating conditions the more accurate the set of selected ofdetermined configuration parameters. Due to number and combinations ofthe properties and the measurable parameters and by contrast to thepresent method an ad-hoc configuration of the system may be not possibleor at least may be a source of inaccuracy.

According to one embodiment, the set of configuration parameterscomprises: an indication of a predistortion of the RF pulse sequence, abias voltage at a predefined point of the RF transmit assembly, a drainvoltage, a matching parameter for tuning a matching network of the RFtransmit assembly, a switch threshold for a toggle switch of the RFamplifier, a temperature of cooling water e.g. used to cool componentsof the RF assembly. The set of configuration parameters may for examplefurther indicate a selection of a shim set in a multi-channel TX systemcomprising the RF transmit assembly as one channel of the multiplechannels. For example, the operating condition of the RF transmitassembly may indicate that there is a need of another RF assembly of themulti-channel TX system to be used. In one example, the operatingcondition may indicate to add e.g. additional local coils to achieve arequired homogeneity.

The matching network may for example be configured for matching a givensystem input impedance with impedances of RF transistors of the RFamplifier for enabling a maximum power transfer. Predistortion may beused to improve the linearity of the output signal. The predistorisionmay be a cost-saving and power efficiency technique. The predistorsiontechnique enables simpler power-electronics in the RF-amplifier and toget more usable power and signal linearity from the RF amplifier. Thismay enable to drive the RF amplifier also in the nonlinear regime. Thetoggle switch may be a RF T/R switch.

According to one embodiment, the method further comprises: receiving atraining set indicative of sets of configuration parameters inassociation with respective operating conditions of the RF transmitassembly; training a predefined machine learning algorithm using thetraining set, thereby generating the machine learning model. Thetraining or learning may be realized by using cloud based algorithms orusing local processor close to or in the RF transmit assembly. Forexample, different machine learning algorithms may be applied based onthe available amount of data of the training set and the availableprocessing resources. The training set may for example be collected frommultiple MRI systems, wherein each MRI system comprises and RF assemblyas described herein. This may increase the accuracy of the generatedmodel. The training may for example be performed on a periodic basis.This may enable up-to-date models for an accurate prediction of thesettings of the RF transmit assembly.

According to one embodiment, the method further comprises generating thetraining set comprising collecting data from at least one data source,and processing the collected data for determining the sets ofconfiguration parameters in association with respective operationconditions, wherein the data source comprises at least one of log filesof MRI systems, user reports indicative of operation of the RF transmitassembly, amplifier load pull data from factories e.g. obtainedindividually or in series (load data may be updated using scan data).This embodiment may enable a rich training set that can be used toprovide reliable and accurate prediction models.

According to one embodiment, the method further comprises repeating thedetermining step for other provided operating conditions, and updatingthe training set using determined configuration parameters and providedoperating conditions and repeating the training of the machine learningalgorithm using the updated training set, wherein the determinedconfiguration parameters are used for operation of the RF transmitassembly. The present disclosure may extend the parameter control of anRF chain by adding a learning module that remembers and reuse successfulcoefficient and parameter settings. This may further increase theaccuracy of the predictions performed by the generated models.

According to one embodiment, using the predefined machine learning modelfor determining at least part of the set of configuration parameters isperformed in response to detecting a current operating condition that isdifferent from the provided operating condition. The RF chain withmachine learning module can respond to changes in the operatingcondition automatically and faster than conventional RF chain devices.

According to one embodiment, the measurable parameter is a physicalproperty of a first component of the RF transmit assembly. The methodfurther comprises: providing multiple machine learning models eachassociated with a component of the RF transmit assembly; selecting thepredefined machine learning model from the multiple machine learningmodels that is associated with the first component.

The present method may use different machine learning algorithms fordifferent RF assembly optimization such as pulse generation, digitalpre-distortion, parameter setting of RF amplifier, impedance matchingoption and RF switch toggle and selection of the shim set inmulti-channel TX systems. For the complete RF assembly, a set oflearning algorithms for machine learning may be applied. For example,the deep learning algorithm may be used to generate a deep learningmodel for determining the values of the configuration parameters of theRF amplifier and a decision tree learning may be used to generate adecision tree model for determining the values of the configurationparameters of the transmit coil. For example, a component (e.g. for RFshimming or matching of individual channels) that requires moreconfiguration parameters than a second component (e.g. bias of LDMOS orcooling water temperature) may be associated with a first learningmethod (deep learning model) while a second learning method (e.g. linearlearning model) may be sufficient for the second component. Havingdifferent algorithms dependent on the component of the RF transmitassembly may further increase the accuracy of the determined settings orconfiguration parameters of the RF transmit assembly.

According to one embodiment, the operating conditions further compriseanother measurable parameter of a second component of the RF transmitassembly. The determining step comprises using the predefined machinelearning model and another machine learning model of the secondcomponent for determining the at least part of the set of configurationparameters and associated values for the operating conditions.

In another aspect, the invention relates to a computer program productcomprising machine executable instructions for execution by a processor,wherein execution of the machine executable instructions causes theprocessor to the methods of any of the preceding embodiments.

In another aspect, the invention relates to a medical analysis system(or medical control system) for configuring a radio frequency, RF,transmit assembly. The medical analysis system comprises: a memorycontaining machine executable instructions; and a processor forcontrolling the medical analysis system, wherein execution of themachine executable instructions causes the medical analysis system to:

provide operating conditions of the RF transmit assembly, the operatingconditions being indicative of at least: a property of the RF pulsesequence and a measurable parameter that influences the RF pulsesequence property when operating the RF transmit assembly using the RFpulse sequence; —use a predefined machine learning model for determiningat least part of the set of configuration parameters and associatedvalues for the operating conditions;

configure the RF transmit assembly in accordance with the determinedconfiguration parameters.

It is understood that one or more of the aforementioned embodiments ofthe invention may be combined as long as the combined embodiments arenot mutually exclusive.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following preferred embodiments of the invention will bedescribed, by way of example only, and with reference to the drawings inwhich:

FIG. 1 is a schematic diagram of a medical analysis system,

FIG. 2 is a schematic representation of an example RF transmit assemblyfor MRI systems,

FIG. 3 is a flowchart of a method for configuring a radio frequency, RF,transmit assembly,

FIG. 4 shows a cross-sectional and functional view of an MRI system.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following, like numbered elements in the figures are eithersimilar elements or perform an equivalent function. Elements which havebeen discussed previously will not necessarily be discussed in laterfigures if the function is equivalent.

Various structures, systems and devices are schematically depicted inthe figures for purposes of explanation only and so as to not obscurethe present invention with details that are well known to those skilledin the art. Nevertheless, the attached figures are included to describeand explain illustrative examples of the disclosed subject matter.

FIG. 1 is a schematic diagram of a medical analysis system 100. Themedical analysis system 100 comprises a control system 111 that isconfigured to connect to a scanning imaging system (or acquisitioncomponent) 101. The control system 111 comprises a processor 103, amemory 107 each capable of communicating with one or more components ofthe medical system 100. For example, components of the control system111 are coupled to a bidirectional system bus 109.

It will be appreciated that the methods described herein are at leastpartly non-interactive, and automated by way of computerized systems.For example, these methods can further be implemented in software 121,(including firmware), hardware, or a combination thereof. In exemplaryembodiments, the methods described herein are implemented in software,as an executable program, and is executed by a special orgeneral-purpose digital computer, such as a personal computer,workstation, minicomputer, or mainframe computer.

The processor 103 is a hardware device for executing software,particularly that stored in memory 107. The processor 103 can be anycustom made or commercially available processor, a central processingunit (CPU), an auxiliary processor among several processors associatedwith the control system 111, a semiconductor based microprocessor (inthe form of a microchip or chip set), a macroprocessor, or generally anydevice for executing software instructions. The processor 103 maycontrol the operation of the scanning imaging system 101.

The memory 107 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM). Note that thememory 107 can have a distributed architecture, where various componentsare situated remote from one another, but can be accessed by theprocessor 103. Memory 107 may store an instruction or data related to atleast one other constituent element of the medical system 100.

The control system 111 may further comprise a display device 125 whichdisplays characters and images and the like e.g. on a user interface129. The display device 125 may be a touch screen display device.

The medical analysis system 100 may further comprise one or more powersupplies 108 for powering the medical analysis system 100. A powersupply of the power supplies 108 may for example be a battery or anexternal source of power, such as electricity supplied by a standard ACoutlet.

The scanning imaging system 101 may comprise at least one of MRI, CT andPET-CT imagers. The control system 111 and the scanning imaging system101 may or may not be an integral part. In other terms, the controlsystem 111 may or may not be external to the scanning imaging system101.

The scanning imaging system 101 comprises components that may becontrolled by the processor 103 in order to configure the scanningimaging system 101 to provide image data to the control system 111. Theconfiguration of the scanning imaging system 101 may enable theoperation of the scanning imaging system 101. The operation of thescanning imaging system 101 may for example be automatic. FIG. 3 showsexample of components of the scanning imaging system 101 being an MRIsystem.

The connection between the control system 111 and the scanning imagingsystem 101 may for example comprise a BUS Ethernet connection, WANconnection, Internet connection etc.

In one example, the scanning imaging system 101 may be configured toprovide output data such as images in response to a specifiedmeasurement. The control system 111 may be configured to receive datasuch as survey image data from the MRI scanning imaging system 101. Forexample, the processor 103 may be adapted to receive information(automatically or upon request) from the scanning imaging system 101 ina compatible digital form so that such information may be displayed onthe display device 125. Such information may include operatingparameters, alarm notifications, and other information related to theuse, operation and function of the scanning imaging system 101.

The medical analysis system 100 may be configured to communicate via anetwork 130 with other scanning imaging systems 131 and/or databases133. The network 130 comprises for example a wireless local area network(WLAN) connection, WAN (Wide Area Network) connection LAN (Local AreaNetwork) connection or a combination thereof. The databases 133 maycomprise information relates to patients, scanning imaging systems,anatomies, scan geometries, scan parameters, scans etc. The databases133 may for example comprise an EMR database comprising patients' EMR,Radiology Information System database, medical image database, PACS,Hospital Information System database and/or other databases comparingdata that can be used for planning a scan geometry. The databases 133may for example comprise training sets used for generating machinelearning models. Additionally or alternatively the training sets may bestored in a local storage (e.g. disk storage or memory) of the controlsystem 111.

The memory 107 may further comprise an artificial intelligence (AI)component 150 (also referred to as machine-learning module). The AIcomponent 150 may or may not be part of software component 121. The AIcomponent 150 may be configured for automatically determining settingsor configuration parameters of a RF transmit assembly of the scanningimaging system 101 based on predefined operating conditions of the RFtransmit assembly.

The AI component 150 may be configured to perform machine learning ontraining sets in order to generate one or more machine learning modelsfor predicting the configuration parameters and their values for the RFtransmit assembly. The AI component 150 may be configured to usedifferent machine learning algorithms for different RF assemblyoptimization or configuration such as pulse generation, digitalpre-distortion, parameter setting of RF amplifier, impedance matchingoption and RF switch toggle and selection of the shim set inmulti-channel TX systems. The generated machine learning models may bestored in a storage are such as memory 107 of the control system 111.

The AI component 150 may for example be configured to generate controlsignals for configuring the RF transmit assembly in accordance with thedetermined or predicted configuration parameters by the machine learningmodels.

The AI component 150 may be configured to recognize when the controllingof the RF transmit assembly has converged, correlate the coefficientsetting of the RF transmit assembly with the measured operatingcondition of the RF transmit assembly, and store both in memory. Forexample, the convergence may be determined by configuring the scanningimaging system 101 (e.g. a MRI system) to acquire image data onreproducible conditions e.g. using a test scan with an unloaded bodycoil which should provide similar outcome for the multiple acquisitionsas an indication of the convergence. The convergence may for example bedetected by observing local distributed RF sensors located in an antennaelement or by directly monitoring the k space data or reconstructed MRimage. The AI component 150 may further be configured to monitor thepresent operating condition and, in response to abrupt changes, restorespast coefficients that were successful under similar conditions.

FIG. 2 is a schematic representation of an example RF transmit assembly200 for MRI systems e.g. the RF transmit assembly 200 may be part of thescanning imaging system 101. The RF assembly of FIG. 2 is shown forexemplification and clarification purpose; other RF assemblies may beconfigured in accordance with the present method. The signal lead 211,for example a coax cable couples the RF power source 215 to the RFtransmission element 213. The RF power source 215 may be implemented asan adjustable RF amplifier. The RF transmission element 213 may beimplemented as a tuned coil loop. The RF transmission element 213 may bea dipole coil element, as used for UHF applications. The loop may belocated in the centre of the dipole as there the electrical current ismaximal. In another example, the RF transmission element 213 may be aTEM coil element or a body coil. The RF amplifier 215 is connected via asignal lead 211 to a T/R switch 210 and a matching network 209. Thematching network 209 is connected to the coil loop 213. A pulsegenerator 207, which may be a digitally controlled transmitter, isconfigured to supply pulsed alternating current signals with definedtiming, phase and amplitude modulation to the RF amplifier 215. Forexample, the pulse generator 207 may generate excitation pulses andprovides these pulses to the RF coil 213 through RF amplifier 215,switch 210 and matching network 209. The RF transmit assembly 200further comprises a power supply 218. The power supply 218 may be a DCpower supply that is configured to convert AC line voltages into DCvoltages that are suitable to operate the RF amplifier 215.

The RF transmit assembly 200 is further provided with a local fieldmonitoring unit 201. The local field monitoring unit 201 is configuredto pick-up a small amount of energy of the transmitted RF (B1) fieldfrom the tuned coil loop 213. The local field monitoring unit 201 may beimplemented as a small pick-up coil. A low puc-signal is generated bythe pick-up coil which is a weak electric voltage or current signal. Thelow puc-signal is fed to a pre-distortion unit 204. The pre-distortionunit 204 is configured to generate using the received pick up coil ordirectional coupler signal coefficient setting for controlling the phaseand magnitude for pulse generation. Signals are then provided to thepulse generator 207 e.g. via a controller 220. The controller 220 isconfigured to control the pulse generator 207 to generate a selected RFwaveform that is used to control the RF amplifier 215 to produce thedrive signal in accordance with the selected RF waveform so that thedesired B1-field is transmitted by the tuned coil loop 213.

The controller 220 is further configured to monitor several operatingparameters of the RF transmit assembly 200 such as DC voltages,currents, pulse width, duty factor, RF output power, temperature of theRF amplifier 215, peak power, average power, pulse shape (linearitydemands), total duration, frequency spread (within the MR bandwidth) ofRF pulses. The operating parameters may further comprise the ambienttemperature or temperature of the cooling water, measured or predictedjunction temperatures of the MOSFETS of the RF amplifier 215, coil load(patient weight and position), power supply voltage, bias voltage etc.

The RF transmit assembly 200 may be configured to connect to the controlsystem 111. The controller 220 which may be an active digital controlleris further configured to receive signals from the control system 111e.g. from the AI component 150. In one example, the controller 220and/or pulse generator 207 may be part of the control system 111.

The RF transmit assembly 200 may be configured through multipleconfiguration parameters. For example, each of the components 204, 207,209, 215 and 218 may be associated with respective configurationparameters. For certain configuration parameters, there are fix absolutelimits, such as maximum peak power of the RF amplifier 215 or maximumcoil currents of the coil 213. Other configuration parameters may dependon the temporal evolution of the RF waveforms, such as the maximumavailable short term average RF power, which depends on the pulse shapeand the RF power applied in the close past.

The RF transmit assembly 200 may be used for a multi-channeltransmission system. For example, multiple transmit systems may be partof the multi-channel transmission system, wherein each transmit systemcomprises the components, other than the coil 213, of the RF transmitassembly 200. The multiple transmit systems are connected to the coil213. In this case, the controller 220 of each of the transmit systemsmay receive input data from other transmit systems e.g. forlinearization and control/sense of coupling between the individualtransmit systems. Components of the RF assembly 200 e.g. the RFamplifier, may be cooled by water chillers.

FIG. 3 is a flowchart of a method for operating the RF transmit assembly200 e.g. in a MRI system such as the one described with reference toFIG. 4.

In step 3001, operating conditions of the RF transmit assembly 200 maybe provided or determined. The operating conditions are indicative of atleast: a property of the RF pulse sequence and a measurable parameterthat influences the RF pulse sequence property when operating the RFtransmit assembly 200 using the RF pulse sequence. For example, theoperating conditions may be desired operating conditions that are userpre-defined. The operating conditions may be the current measuredoperating conditions.

For example, the value of the peak power demand (requirement) from theRF amplifier 215 may be determined from patient data and/or RF pulses.In addition, the temperature of the cooling water may be measured. Thetwo measured values may be provided as input to the AI component 150.The two measured values may indicate an example of the current operatingconditions of the RF transmit assembly 200.

In step 3003, using the input operating conditions, a predefined machinelearning model may be used e.g. by the AI component 150 for determiningat least part of the set of configuration parameters and associatedvalues. Following the above example, the demand values of the peak powerand the cooling water temperature may be provided as input to a deeplearning model that has been generated by the AI component 150. Usingthe input values, the deep learning model may identify a subset of theset of configuration parameters of the RF transmit assembly 200 thatneeds to be set or changed in order to match the current input operatingconditions. For example, if the peak power is smaller than the peakpower demand in a previous iteration or as initially set, the powersupply of the RF amplifier may be configured so as to provide aconvenient DC supply value for enabling the current level for thecurrent value of the peak power. And if the cooling water temperature ishigher than a maximum predefined temperature threshold, a source ofpower dissipation such as the DC supply may be decreased so as to keepthe temperature to a predefined level. However, since the value of theselected configuration parameter, e.g. DC supply, may affect the twooperating conditions (e.g. and affect other operating conditions such asscan time which may be due to a reduced allowed average power of the RFpulse because of a chosen DC supply), a value for the DC supply may bedifficult to set by a user. The deep learning model may be able todetermine the optimal value of the DC supply that can be used formeeting both current operating conditions.

The deep learning model may for example be generated using the AIcomponent 150. The AI component 150 may for example receive a trainingset comprising sets of configuration parameters in association withrespective operating conditions of the RF transmit assembly 200. Forexample, log files may be received from multiple MRI systems by the AIcomponent 150, wherein the log files are indicative of the operatingconditions and associated set of configuration parameters. For example,successful coefficient settings or sets of configuration parameters maybe stored in a list that is indexed using a multi-dimensional attributevector derived from the measured operating conditions. The size of thelist is dynamic, growing, as more operating conditions are experiencedand contracting as neighboring elements are recognized as redundant. Thefollowing table provides an example of sets of configuration parametersand associated operating conditions (configurations are referred to asset1 and set2 and respective operating conditions as OC1 and OC2). Theoperating conditions are determined in this example by the values of theVoltage Standing Wave Ratio (VSWR), coil load factor and the B1 peakvalue. The associated configuration parameters are the Vdd, bias currentIbias (e.g. at MOSFTS) and the number of MOSFETS to be used.

Coil B1 Vdd Ibias Number of load peak set In V In mA MOSFETS OC VSWRfactor uT Set1 50 300 6 OC1 1.25 1.1 10 Set2 55 500 8 OC2 2 1.5 13.5

The AI component 150 may run a predefined machine learning algorithmsuch as a deep learning algorithm, using the training set in order togenerate the machine learning model. The training may for example beexecuted on a cloud platform e.g. Algorithms.io. This may particularlybe advantageous for machine learning algorithms to classify streamingdata from connected systems e.g. multiple MRI systems providing data ofthe training set to the AI component 150.

In step 3005, the RF transmit assembly 200 may be configured inaccordance with the determined configuration parameters. Following theabove example, the DC supply may be configured using the valuesdetermined by the deep learning model. The configured RF transmitassembly may be used in a MRI system for acquiring image data from asubject in an imaging zone of the MRI system.

Steps 3001-3005 may for example be repeated on a periodic basis e.g.every day or in response to a change in the operating conditions. Forexample, if the current operating conditions are different from theoperating conditions of the last iteration, steps 3001-3005 may berepeated. For example, the controller 220 may be configured to modifypredistortion coefficient setting continually and other parameters suchas bias, V drain, matching, switch toggling as the operating conditionchanges.

FIG. 4 illustrates a magnetic resonance imaging system 300 as an exampleof the medical system 100. The magnetic resonance imaging system 300comprises a magnet 304. The magnet 304 is a superconducting cylindricaltype magnet with a bore 306 in it. The use of different types of magnetsis also possible; for instance, it is also possible to use both a splitcylindrical magnet and a so called open magnet. A split cylindricalmagnet is similar to a standard cylindrical magnet, except that thecryostat has been split into two sections to allow access to theiso-plane of the magnet. Such magnets may for instance be used inconjunction with charged particle beam therapy. An open magnet has twomagnet sections, one above the other with a space in-between that islarge enough to receive a subject 318 to be imaged, the arrangement ofthe two sections area similar to that of a Helmholtz coil. Inside thecryostat of the cylindrical magnet there is a collection ofsuperconducting coils. Within the bore 306 of the cylindrical magnet 304there is an imaging zone or volume or anatomy 308 where the magneticfield is strong and uniform enough to perform magnetic resonanceimaging.

Within the bore 306 of the magnet there is also a set of magnetic fieldgradient coils 310 which is used during acquisition of magneticresonance data to spatially encode magnetic spins of a target volumewithin the imaging volume or examination volume 308 of the magnet 304.The magnetic field gradient coils 310 are connected to a magnetic fieldgradient coil power supply 312. The magnetic field gradient coils 310are intended to be representative. Typically, magnetic field gradientcoils 310 contain three separate sets of coils for the encoding in threeorthogonal spatial directions. A magnetic field gradient power supplysupplies current to the magnetic field gradient coils. The currentsupplied to the magnetic field gradient coils 310 is controlled as afunction of time and may be ramped or pulsed.

MRI system 300 further comprises an RF coil 314 at the subject 318 andadjacent to the examination volume 308 for generating RF excitationpulses. The RF coil 314 may include for example a set of surface coilsor other specialized RF coils. The RF coil 314 may be used alternatelyfor transmission of RF pulses as well as for reception of magneticresonance signals e.g., the RF coil 314 may be implemented as a transmitarray coil comprising a plurality of RF transmit coils. The RF coil 314is connected to one or more RF amplifiers 315. Elements 314-315 may forman example RF transmit assembly of the MRI system 300.

The magnetic field gradient coil power supply 312 and the RF amplifier315 are connected to a hardware interface of control system 111. Thememory 107 of control system 111 may for example comprise a controlmodule. The control module contains computer-executable code whichenables the processor 103 to control the operation and function of themagnetic resonance imaging system 300. It also enables the basicoperations of the magnetic resonance imaging system 300 such as theacquisition of magnetic resonance data.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as an apparatus, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a ‘circuit’, ‘module’ or ‘system’.Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer executable code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A ‘computer-readablestorage medium’ as used herein encompasses any tangible storage mediumwhich may store instructions which are executable by a processor of acomputing device. The computer-readable storage medium may be referredto as a computer-readable non-transitory storage medium. Thecomputer-readable storage medium may also be referred to as a tangiblecomputer readable medium. In some embodiments, a computer-readablestorage medium may also be able to store data which is able to beaccessed by the processor of the computing device. Examples ofcomputer-readable storage media include, but are not limited to: afloppy disk, a magnetic hard disk drive, a solid state hard disk, flashmemory, a USB thumb drive, Random Access Memory (RAM), Read Only Memory(ROM), an optical disk, a magneto-optical disk, and the register file ofthe processor. Examples of optical disks include Compact Disks (CD) andDigital Versatile Disks (DVD), for example CD-ROM, CD-RW, CD-R, DVD-ROM,DVD-RW, or DVD-R disks. The term computer readable-storage medium alsorefers to various types of recording media capable of being accessed bythe computer device via a network or communication link. For example, adata may be retrieved over a modem, over the internet, or over a localarea network. Computer executable code embodied on a computer readablemedium may be transmitted using any appropriate medium, including butnot limited to wireless, wireline, optical fiber cable, RF, etc., or anysuitable combination of the foregoing.

A computer readable signal medium may include a propagated data signalwith computer executable code embodied therein, for example, in basebandor as part of a carrier wave. Such a propagated signal may take any of avariety of forms, including, but not limited to, electro-magnetic,optical, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

A ‘computer memory’ or ‘memory’ is an example of a computer-readablestorage medium. A computer memory is any memory which is directlyaccessible to a processor. A ‘computer storage’ or ‘storage’ is afurther example of a computer-readable storage medium. A computerstorage is any non-volatile computer-readable storage medium. In someembodiments computer storage may also be computer memory or vice versa.

A ‘processor’ as used herein encompasses an electronic component whichis able to execute a program or machine executable instruction orcomputer executable code. References to the computing device comprising‘a processor’ should be interpreted as possibly containing more than oneprocessor or processing core. The processor may for instance be amulti-core processor. A processor may also refer to a collection ofprocessors within a single computer system or distributed amongstmultiple computer systems. The term computing device should also beinterpreted to possibly refer to a collection or network of computingdevices each comprising a processor or processors. The computerexecutable code may be executed by multiple processors that may bewithin the same computing device or which may even be distributed acrossmultiple computing devices.

Computer executable code may comprise machine executable instructions ora program which causes a processor to perform an aspect of the presentinvention. Computer executable code for carrying out operations foraspects of the present invention may be written in any combination ofone or more programming languages, including an object-orientedprogramming language such as Java, Smalltalk, C++ or the like andconventional procedural programming languages, such as the ‘C’programming language or similar programming languages and compiled intomachine executable instructions. In some instances, the computerexecutable code may be in the form of a high-level language or in apre-compiled form and be used in conjunction with an interpreter whichgenerates the machine executable instructions on the fly.

The computer executable code may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block or a portion of theblocks of the flowchart, illustrations, and/or block diagrams, can beimplemented by computer program instructions in form of computerexecutable code when applicable. It is further understood that, when notmutually exclusive, combinations of blocks in different flowcharts,illustrations, and/or block diagrams may be combined. These computerprogram instructions may be provided to a processor of a general-purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

A ‘user interface’ as used herein is an interface which allows a user oroperator to interact with a computer or computer system. A ‘userinterface’ may also be referred to as a ‘human interface device’. A userinterface may provide information or data to the operator and/or receiveinformation or data from the operator. A user interface may enable inputfrom an operator to be received by the computer and may provide outputto the user from the computer. In other words, the user interface mayallow an operator to control or manipulate a computer and the interfacemay allow the computer indicate the effects of the operator's control ormanipulation. The display of data or information on a display or agraphical user interface is an example of providing information to anoperator. The receiving of data through a keyboard, mouse, trackball,touchpad, pointing stick, graphics tablet, joystick, gamepad, webcam,headset, gear sticks, steering wheel, pedals, wired glove, dance pad,remote control, and accelerometer are all examples of user interfacecomponents which enable the receiving of information or data from anoperator.

A ‘hardware interface’ as used herein encompasses an interface whichenables the processor of a computer system to interact with and/orcontrol an external computing device and/or apparatus. A hardwareinterface may allow a processor to send control signals or instructionsto an external computing device and/or apparatus. A hardware interfacemay also enable a processor to exchange data with an external computingdevice and/or apparatus. Examples of a hardware interface include, butare not limited to: a universal serial bus, IEEE 1394 port, parallelport, IEEE 1284 port, serial port, RS-232 port, IEEE-488 port, Bluetoothconnection, Wireless local area network connection, TCP/IP connection,Ethernet connection, control voltage interface, MIDI interface, analoginput interface, and digital input interface.

A ‘display’ or ‘display device’ as used herein encompasses an outputdevice or a user interface adapted for displaying images or data. Adisplay may output visual, audio, and or tactile data. Examples of adisplay include, but are not limited to: a computer monitor, atelevision screen, a touch screen, tactile electronic display, Braillescreen, Cathode ray tube (CRT), Storage tube, Bistable display,Electronic paper, Vector display, Flat panel display, Vacuum fluorescentdisplay (VF), Light-emitting diode (LED) displays, Electroluminescentdisplay (ELD), Plasma display panels (PDP), Liquid crystal display(LCD), Organic light-emitting diode displays (OLED), a projector, andHead-mounted display.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments.

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practicing the claimedinvention, from a study of the drawings, the disclosure, and theappended claims. In the claims, the word ‘comprising’ does not excludeother elements or steps, and the indefinite article ‘a’ or ‘an’ does notexclude a plurality. A single processor or other unit may fulfill thefunctions of several items recited in the claims. The mere fact thatcertain measures are recited in mutually different dependent claims doesnot indicate that a combination of these measured cannot be used toadvantage. A computer program may be stored/distributed on a suitablemedium, such as an optical storage medium or a solid-state mediumsupplied together with or as part of other hardware, but may also bedistributed in other forms, such as via the Internet or other wired orwireless telecommunication systems. Any reference signs in the claimsshould not be construed as limiting the scope.

LIST OF REFERENCE NUMERALS

-   100 medical system-   101 scanning imaging system-   103 processor-   107 memory-   108 power supply of a medical analysis system-   109 bus-   111 control system-   121 software-   125 display-   129 user interface-   150 AI component-   200 RF assembly-   201 pick up coil-   204 predistorsion system-   207 pulse generator-   209 matching network-   210 switch-   211 signal lead-   213 coil-   215 RF amplifier-   218 power supply of a RF transmit assembly-   220 controller-   3001-3005 method steps-   300 magnetic resonance imaging system-   304 magnet-   306 bore of magnet-   308 imaging zone-   310 magnetic field gradient coils-   312 magnetic field gradient coil power supply-   314 radio-frequency coil-   315 RF amplifier-   318 subject.

The invention claimed is:
 1. A method for configuring a radio frequency (RF) transmit assembly for a magnetic resonance imaging system for acquiring magnetic resonance imaging data from a subject within an imaging zone using an RF pulse sequence, the RF transmit assembly comprising an RF amplifier and a transmit coil, wherein the RF transmit assembly is configurable with a set of configuration parameters, the method comprising: providing operating conditions of the RF transmit assembly, the operating conditions being indicative of at least: a property of the RF pulse sequence and a measurable parameter that influences the RF pulse sequence property when operating the RF transmit assembly using the RF pulse sequence, wherein the measurable parameter comprises at least one of ambient temperature, temperature of a cooling water of the RF transmit assembly, junction temperatures of components of the RF amplifier, coil load of the transmit coil, power supply voltage of a power supply of a medical analysis system, power supply voltage of a power supply of the RF transmit assembly, the operating conditions being further indicative of a subject weight, subject height, imaging position, anatomy to be scanned; using a predefined machine learning model for determining, for the operating conditions, at least part of the set of configuration parameters and associated values; configuring the RF transmit assembly in accordance with the determined configuration parameters and associated values.
 2. The method of claim 1, the property of the pulse sequence comprising at least one of a: peak power, average power, pulse shape, total duration and frequency distribution of the pulse.
 3. The method of claim 1, wherein the set of configuration parameters comprises at least one of an indication of a predistortion of the RF pulse sequence, a bias voltage at a predefined point of the RF transmit assembly, a drain voltage, a matching parameter for tuning a matching network of the RF transmit assembly, a switch threshold for a toggle switch of the RF amplifier.
 4. The method of claim 1, further comprising: receiving a training set indicative of sets of configuration parameters in association with respective operating conditions of the RF transmit assembly; training a predefined machine learning algorithm using the training set, thereby generating the machine learning model.
 5. The method of claim 4, further comprising generating the training set comprising collecting data from at least one data source, and processing the collected data for determining the sets of configuration parameters in association with respective operation conditions, wherein the data source comprises at least one of log files of MRI systems, user reports indicative of operation of the RF transmit assembly.
 6. The method of claim 4, further comprising repeating the determining step for other provided operating conditions, and updating the training set using the determined configuration parameters and all provided operating conditions and repeating the training of the machine learning algorithm using the updated training set, wherein the determined configuration parameters are used for operation of the RF transmit assembly.
 7. The method of claim 1, wherein using the predefined machine learning model for determining at least part of the set of configuration parameters is performed in response to detecting that the provided operating conditions are different from previously provided operating conditions.
 8. The method of claim 1, wherein the measurable parameter is of physical property of a first component of the RF transmit assembly; the method further comprising: providing multiple machine learning models each associated with a component of the RF transmit assembly; selecting the predefined machine learning model from the multiple machine learning models that is associated with the first component.
 9. The method of claim 8, wherein the operating conditions further comprise another measurable parameter of a second component of the RF transmit assembly, the determining step comprising using the predefined machine learning model and another selected machine learning model of the multiple models for the second component for determining the at least part of the set of configuration parameters and associated values for the operating conditions.
 10. A computer program product comprising machine executable instructions stored on a non-transitory computer readable medium for execution by a processor, wherein execution of the machine executable instructions causes the processor to perform the method of claim
 1. 11. A medical analysis system for configuring a radio frequency, RF transmit assembly, comprising: a memory containing machine executable instructions; and a processor for controlling the medical analysis system, wherein execution of the machine executable instructions causes the medical analysis system to: provide operating conditions of the RF transmit assembly, the operating conditions being indicative of at least: a property of the RF pulse sequence and a measurable parameter that influences the RF pulse sequence property when operating the RF transmit assembly using the RF pulse sequence, wherein the measurable parameter comprises at least one of ambient temperature, temperature of a cooling water of the RF transmit assembly, junction temperatures of components of the RF amplifier, coil load of the transmit coil, power supply voltage of a power supply of a medical analysis system, power supply voltage of a power supply of the RF transmit assembly, the operating conditions being further indicative of a subject weight, subject height, imaging position, anatomy to be scanned; use a predefined machine learning model for determining at least part of the set of configuration parameters and associated values for the operating conditions; configure the RF transmit assembly in accordance with the determined configuration parameters.
 12. The medical analysis system of claim 11, being configured to connect to multiple RF assemblies and to receive from the RF assemblies data of a training set indicative of sets of configuration parameters in association with respective operating conditions of the RF transmit assemblies.
 13. The medical analysis system of claim 11 further comprising the RF transmit assembly.
 14. A MRI system comprising the medical analysis system of claim 11, the MRI system being configured for acquiring image data. 